NOISE REDUCTION SYSTEM AND METHOD FOR AN URBAN AIR MOBILITY SYSTEM

Abstract

Noise reduction system for an urban air mobility system (UAM, 100) comprising: the UAM (100) configured to detect and provide information on the revolutions per minute (RPM) of a propeller and location coordinate information; a noise suppression device (200) configured to predict a noise suppression sound wave amplitude based on the RPM information of the propeller and the location coordinate information received via the UAM (100), and to output a noise suppression sound wave corresponding to the predicted noise suppression sound wave amplitude to the UAM (100); and a microphone (400) configured to receive noise generated in response to the takeoff or landing of the UAM (100), characterized by a noise suppression sound wave calculator (500) configured to calculate an offset wave using the noise received by the microphone (400);and a verifier (600) trained to verify a predicted noise suppression sound wave amplitude with the offset wave and to manage the verified noise suppression sound wave amplitude by storing the noise suppression sound wave amplitude in a database.

Description

CROSS-REFERENCE TO RELATED REGISTRATIONS The present application claims the benefit of Korean patent application No. 10-2021-0065335, filed on November 21, 2021 with the Korean Intellectual Property Office, the entire disclosure of which is incorporated by reference for all purposes into the present application. BACKGROUND 1. Field of the invention The present disclosure relates to a noise reduction method. 2. Discussion of the state of the art Urban air mobility systems (hereinafter referred to as “UAMs”), which transcend temporal and spatial limitations, are emerging as pioneers for future urban mobilities. In recent years, various announcements of strategies for the development of UAM, such as EHang, Uber and Toyota, have proven worldwide that three-dimensional (3D) air transport is a key to next-generation mobility. The Korean Ministry of Land, Infrastructure and Transport announced that so-called Urban Sky Roads will be opened in 2025, and the Hyundai Motor Group presented UAM models on the topic of future mobility solutions at CES2020 in January 2020. One of the many pressing problems facing UAM is noise. In general, airplanes are means of transport that cause a lot of noise, and large drones also cause enormous noise. UAMs, i.e., aircraft that fly through cities, cannot be marketed if the UAMs are as loud as existing aircraft. Uber's low noise standard is 62 dB at 500 feet, and many companies are trying to address this issue. Several attempts have been made to reduce the noise of existing UAMs, for example by choosing an electric-powered vertical take-off and landing system instead of a motor, or by using distributed rotors instead of a single rotor. However, noise cannot be perfectly reduced solely by analyzing and eliminating a noise source from an aircraft, so an additional noise reduction method using other methods could represent a new solution. US 2017 / 0 154 618 A1 discloses a noise reduction system for an urban air mobility system with the features of the preamble of claim 1. Further noise reduction systems for an urban air mobility system are disclosed in US 2019 / 0 108 827 A1 and US 10 176 792 B1. US 2020 / 0 001 469 A1 discloses a robot with a noise reduction system. OVERVIEW OF THE INVENTION This overview serves to present a selection of concepts in simplified form, which are described in more detail below. It is not intended to identify key features or essential characteristics of the claimed subject matter, nor is it meant to assist in determining the scope of the claimed subject matter. From a general perspective, a noise reduction system for an urban air mobility system (UAM) is provided, comprising a UAM configured to acquire and provide information on the revolutions per minute (RPM) of a propeller and location coordinate information, and comprising a noise suppression device configured to predict a noise suppression sound wave amplitude based on the RPM information of the propeller and the location coordinate information received via the UAM, and to output a noise suppression sound wave corresponding to the predicted noise suppression sound wave amplitude to the UAM. The noise suppression device may comprise: a communicator configured to receive the propeller's RPM information and the location coordinate information by communicating with the UAM; a deep learning machine configured to predict the noise suppression sound wave amplitude based on the propeller's RPM information and the UAM's location coordinate information received via the communicator; one or more loudspeakers configured to output the noise suppression sound wave; and an output controller configured to control the one or more loudspeakers to output the noise suppression sound wave amplitude predicted by the deep learning machine. The UAM noise reduction system can include a weather information input device trained to receive weather information, whereby the deep learning machine can predict the noise reduction sound wave amplitude based on the propeller's RPM information, the UAM's location coordinate information, and the weather information. According to the invention, the UAM noise reduction system comprises a microphone configured to receive noise generated in response to the takeoff or landing of a UAM, and a noise reduction sound wave calculator configured to calculate an offset wave using the noise received by the microphone. According to the invention, the UAM noise reduction system further comprises a verifier configured to verify a predicted noise reduction sound wave amplitude using the offset wave and to manage the verified noise reduction sound wave amplitude by storing the noise reduction sound wave amplitude in a database. According to another general aspect, a processor-implemented noise reduction procedure for an urban air mobility (UAM) system is envisaged, which includes: receiving information on the revolutions per minute (RPM) of a propeller and location coordinate information from a UAM; determining whether the collected data is sufficient by analyzing an interaction between the RPM information and the location information, which are factors received from the UAM; predicting a noise reduction sound wave amplitude by applying the received factors to a deep learning machine in response to the finding that the collected data is sufficient;Storing information on the factors used in predicting the noise reduction sound wave amplitude in a database, in response to the finding that the collected data meet a reference reliability; outputting the predicted noise reduction sound wave amplitude via a loudspeaker; and receiving noise information via a microphone. The UAM noise reduction procedure further includes calculating a noise reduction sound wave amplitude using the noise information captured via the microphone, in response to the finding that the collected data are insufficient, wherein, upon finding that the collected data meet a reference reliability, the predicted noise reduction sound wave amplitude stored in the database is verified by comparison with an offset wave, wherein the offset wave is calculated from the noise information received by means of the microphone. The UAM noise reduction procedure may include determining whether weather information is being inputted and analyzing the interaction between the factors using the propeller's RPM information and the location and weather information received by the UAM, in response to the finding that weather information is being inputted. The UAM noise reduction procedure may involve receiving weather information from a weather authority server through web crawling or deep learning of weather information using the database, in response to the finding that no weather information is being input. According to a general aspect, which is not part of the claims, a noise reduction system for an urban air mobility (UAM) system is provided, comprising: offset wave output devices provided at a mobility stop and configured to output offset waves to compensate for the noise generated by a UAM; a microphone configured to receive noise generated in response to the takeoff or landing of a UAM; a noise reduction sound wave computer configured to calculate offset waves using the noise received by the microphone; and an output controller configured to control the offset waves to be output to the UAM via the loudspeakers. Further features and aspects will become apparent from the following detailed description, drawings and claims. BRIEF DESCRIPTION OF THE DRAWINGS The above and other problems, features, and advantages of the present disclosure will become clearer to the person skilled in the art by the detailed description of exemplary embodiments thereof with reference to the accompanying drawings, which show: Fig. 1 shows a configuration diagram to describe a noise reduction system for an urban air mobility (UAM) system according to one embodiment of the present disclosure; Fig. 2 shows a block diagram to describe a detailed configuration of a noise reduction device from Fig. 1; Fig. 3 shows a block diagram to describe a UAM noise reduction system according to another embodiment of the present disclosure; Fig. 4 shows a flowchart of a UAM noise reduction method according to one embodiment of the present disclosure; and Fig. 5 shows a block diagram to describe a UAM noise reduction system according to another embodiment of the present disclosure. In the drawings and the detailed description, the same reference symbols refer to the same elements, features, and structures unless otherwise described or specified. The drawings may not be to scale, and the relative size, proportions, and representation of elements in the drawings may be exaggerated for clarity, illustration, and simplicity. DETAILED DESCRIPTION OF EXAMPLES OF EXECUTION The following detailed description is intended to help the reader gain a comprehensive understanding of the methods, devices, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, devices, and / or systems described herein will become apparent upon understanding the disclosure of this application. For example, the sequences of operations described here are merely examples and are not limited to those presented here, but may be modified as is evident from understanding the disclosure of this application, with the exception of operations that necessarily occur in a specific sequence. Furthermore, descriptions of features known in the prior art may be omitted for the sake of greater clarity and conciseness. The features described herein can be implemented in various forms and are not to be interpreted as limiting the examples described herein. Rather, the examples described herein are provided merely to illustrate some of the many possible ways of implementing the methods, devices and / or systems described herein, which are obvious from an understanding of the disclosure of this application. The terminology used herein serves only to describe certain examples and is not intended to limit disclosure. The singular forms "a" and "the" are intended to include the plural forms unless the context clearly indicates otherwise. The expression "and / or" used herein includes any and all combinations of two or more of the related listed elements. The expressions "contain," "exhibit," and "have" used herein denote the presence of the aforementioned features, numbers, processes, elements, components, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, processes, elements, components, and / or combinations thereof. When a component is described in the specification as "connected to" or "coupled with" another component, it may be directly connected or coupled to that component, or it may involve one or more other interposed components. Conversely, when an element is described as "directly connected to" or "directly coupled with" another element, no interposed elements are present. Similarly, expressions such as "between" and "immediately between," as well as "adjacent to" and "immediately adjacent to," are to be interpreted in the same way. The term "and / or" used herein encompasses any and all combinations of two or more of the related listed elements. While the terms "first," "second," and "third" can be used here to describe different elements, components, areas, layers, or sections, these elements, components, areas, layers, or sections should not be restricted by these terms. Rather, these terms merely serve to distinguish one element, component, area, layer, or section from another. Thus, a first element, first component, first area, first layer, or first section referred to in the examples described here can also be called a second element, second component, second area, second layer, or second section without deviating from the lesson taught in the examples. Fig. 1 shows a configuration diagram to describe a noise reduction system for an urban air mobility (UAM) system according to an embodiment of the present disclosure. The UAM noise reduction system according to the embodiment of the present disclosure comprises a UAM 100 and a noise suppression device 200. The UAM 100 is a means of transport that can take off or land vertically using a variety of rotors and detects information on the revolutions per minute (RPM) of a propeller and location coordinate information, and provides the RPM information and location coordinate information to the noise suppression device 200. The noise suppression device 200 predicts a noise suppression sound wave amplitude based on the propeller's RPM information and the location coordinate information received via the UAM 100, and outputs a noise suppression sound wave corresponding to the predicted noise suppression sound wave amplitude to the UAM 100. In one embodiment of the present disclosure, the noise suppression device 200 comprises a communicator 210, a deep learning machine 220, a loudspeaker 230 and an output controller 240, as shown in Fig. 2. The communicator 210 receives RPM information from the propeller and location information through communication with the UAM 100. In the present embodiment, the communicator 210 is not limited and various types of communicator devices can be used as communicator 210. The deep learning machine 220 predicts a noise suppression sound wave amplitude from the RPM information of the propeller and the location information to the UAM 100, which is received via the communicator 210. The loudspeaker 230 emits a noise-canceling sound wave, and a large audio system can be formed by installing one or more loudspeakers 230 at a mobility stop. The output control 240 controls at least one loudspeaker 230 in such a way that it outputs the noise suppression sound wave amplitude predicted by the deep learning machine 220. According to one embodiment of the present disclosure, it is possible to reduce noise generated by a UAM when starting up or charging at a mobility stopover. According to one embodiment of the present disclosure, it is possible to significantly reduce a certain noise level, including rotor noise, motor rotation noise, motor noise, etc., generated by a UAM. Furthermore, according to one embodiment of the present disclosure, the noises can be automatically controlled depending on the location of a user or on the environmental conditions. Fig. 3 shows a block diagram to describe a UAM noise reduction system according to another embodiment of the present disclosure. In another embodiment of the present disclosure, the UAM noise reduction system can further comprise a weather information input device 300, and a deep learning machine 220 can predict a noise reduction sound wave amplitude by further considering weather information. In the present embodiment, the weather information input device 300 can acquire weather information directly from sensing devices, such as a thermometer, a hygrometer, an airflow meter, and a rain gauge, or it can acquire weather information by web crawling on a website of the Korean Meteorological Agency (KMA). As described above, weather information can also be taken into account when predicting a noise reduction sound wave amplitude to increase a noise reduction effect by accurately compensating for a change in the noise reduction sound wave amplitude according to a weather condition during the takeoff or landing of the same UAM. In another embodiment of the present disclosure, the UAM noise reduction system may further comprise a microphone 400 and a noise reduction sound wave computer 500. The microphone 400 receives the actual noise generated during takeoff or landing of the UAM. The noise suppression sound wave calculator 500 can digitize the actual noise generated by the UAM 100 and received via the Mikrofron 400 and calculate an offset wave with an inverse phase wavelength. Therefore, in an embodiment of the present disclosure, the UAM noise reduction system may further comprise a verification unit or a verifier 600. The Verification Unit or Verifier 600 can verify the noise reduction sound wave amplitude predicted from the noise reduction sound wave amplitude calculated by the Noise Reduction Sound Wave Calculator 500. For example, the Verification Unit or Verifier 600 can verify a noise reduction sound wave amplitude predicted from the offset wave calculated by the Noise Reduction Sound Wave Calculator 500. The Verification Unit or Verifier 600 can manage the noise reduction sound wave amplitude by storing it in a database, thereby improving the accuracy of its prediction. A UAM noise reduction method according to an embodiment of the present disclosure is described below with reference to Fig. 4. First, RPM information from a propeller and location information are received from a UAM (S110). Next, it is determined whether weather information is being entered (S120). If, during the determination of whether weather information is being input (S120), it is determined that weather information is being input (YES), an interaction between factors is analyzed to determine whether the collected data is sufficient (S130). Factors analyzed in one embodiment of the present disclosure may include the propeller's RPM information and the location information, or may include the propeller's RPM information, the location information, and the weather information. If, when determining whether the collected data is sufficient (S130), it is found that the collected data is sufficient (YES), a noise suppression sound wave amplitude is predicted by applying input factors to a deep learning machine (S140). The predicted noise-suppression sound wave amplitude is then output through a loudspeaker (S150). If, when determining whether weather information is being entered (S120), it is determined that weather information is not being entered (NO), weather information is received from a weather authority server by web crawling or is learned by deep learning using the database of the UAM noise reduction system (S121). After the noise suppression sound wave amplitude is predicted (S140), the predicted noise suppression sound wave amplitude is managed by storing it in the database. For this purpose, it is determined whether the collected data meets the reference reliability. If, during the verification process, the collected data is found to meet the reference reliability, information about the factors used in predicting the noise reduction sound wave amplitude is stored in the database. The noise reduction sound wave amplitude stored and learned in the database can be verified by comparison with a noise reduction sound wave amplitude calculated from noise captured by the microphone 400. If, when determining whether the collected data is sufficient (S130), it is determined that the collected data is insufficient (NO), noise information is captured by the microphone 400 (S160) and a noise suppression sound wave amplitude is calculated using information about the noise from the UAM 100 (S170) and then output through the loudspeakers (S150). Fig. 5 shows a block diagram to describe a UAM noise reduction system according to another embodiment of the present disclosure. As shown in Fig. 5, the UAM noise reduction system according to the embodiment of the present disclosure comprises a microphone 400, a noise reduction sound wave computer 500 and a loudspeaker 230. The microphone 400 receives the actual noise generated during takeoff or landing of the UAM. The noise suppression sound wave calculator 500 can calculate an offset wave using the actual noise generated by the UAM 100 and received by the microphone 400. A large number of loudspeakers 230 can be provided at a mobility stop to output an offset wave to compensate for noise generated by the UAM 100. The output control 240 controls the calculated offset wave to be output to the UAM 100 through the multitude of loudspeakers 230. As described above, according to another embodiment of the present disclosure, a UAM noise reduction system may be provided at a mobility stopover to receive actual noise generated during the takeoff or landing of a UAM through a microphone, calculate an offset wave from the actual noise received by the microphone, and output the calculated offset wave to the UAM through a plurality of loudspeakers, thereby reducing noise generated during the takeoff or landing of the UAM. According to one embodiment of the present disclosure, a noise problem, which is a major problem of an air mobility system during takeoff or landing, can be solved with the help of artificial intelligence and active noise control (ANC) of a HUB audio system. As described above, a noise reduction device for an urban air mobility (UAM) system is provided in which a mobility hub (HUB) is designed as a single large audio device to reduce noise during the takeoff and landing of a UAM using active noise control (ANC) and artificial intelligence. Although the embodiments of this disclosure have been described in detail above with reference to the accompanying drawings, these embodiments are merely examples, and various modifications and changes can be made within the scope of this disclosure by a person skilled in the art in the field to which this disclosure relates. Therefore, the scope of this disclosure is not limited to the aforementioned embodiments and is to be defined by the attached claims. Each step in the learning process described above can be implemented as a software module, a hardware module, or a combination thereof, which is executed by a computing device. Furthermore, an element for carrying out each step can be implemented as the first or second operation logic of a processor. Exemplary procedures according to embodiments may, for the sake of clarity, be expressed as a series of operations; however, such a step does not restrict the sequence in which operations are performed. Depending on the case, the steps may be performed simultaneously or in different sequences. To implement a method according to embodiments, a disclosed step may additionally include another step, include other steps than some steps, or include a different additional step than some steps. The various embodiments of the present disclosure do not list all available combinations, but serve to describe a representative aspect of the present disclosure, and the descriptions of different embodiments can be applied independently of one another or can be applied by a combination of two or more. The devices, units, modules, and components described herein are implemented by hardware components. Examples of hardware components that can be used to perform the operations described in this application include controllers, sensors, generators, drivers, memory, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and all other electronic components configured to perform the operations described in this application. In other examples, one or more of the hardware components that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers. A processor or computer can be implemented by one or more processing elements, e.g.,an arrangement of logic gates, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field-programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices configured to respond to instructions and execute them in a defined manner to achieve a desired result. For example, a processor or computer has or is associated with one or more memories, the memories storing instructions or software to be executed by the processor or computer. Hardware components implemented by a processor or computer can execute instructions or software, such as...An operating system (OS) and one or more software applications that run on the OS to perform the operations described in this application. The hardware components can also access, manipulate, process, create, and store data in response to the execution of instructions or software. For simplicity, the singular term "processor" or "computer" may be used in the description of the examples described in this application; however, in other examples, multiple processors or computers may be used, or a processor or computer may have multiple processing elements or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components may be implemented by a single processor, two or more processors, or a processor and a controller.One or more hardware components can be implemented by one or more processors or by a processor and a controller, and one or more other hardware components can be implemented by one or more other processors or by a different processor and a different controller. One or more processors or a processor and a controller can implement a single hardware component or two or more hardware components. A hardware component can have one or more different processing configurations, e.g.,a single processor, independent processors, parallel processors, SISD multiprocessing (Single Instruction Single Data), SIMD multiprocessing (Single Instruction Multiple Data), MISD multiprocessing (Multiple Instruction Single Data), MIMD multiprocessing (Multiple Instruction Multiple Data), a controller and an arithmetic logic unit (ALU), a DSP, a microcomputer, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic unit (PLU), a central processing unit (CPU), a graphics processing unit (GPU), a neural processing unit (NPU), or any other device capable of responding to instructions and executing them in a defined manner. The methods that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers, which are implemented as described above to execute instructions or software to perform the operations described in this application. For example, a single operation or two or more operations can be performed by a single processor, two or more processors, or a processor and a controller. One or more operations can be performed by one or more processors or a processor and a controller, and one or more operations can be performed by one or more other processors or another processor and another controller.One or more processors, or one processor and one controller, can perform a single operation or two or more operations. Instructions or software for controlling a processor or computer to implement the hardware components and perform the procedures as described above are written as computer programs, code segments, instructions, or a combination thereof, to instruct or configure the processor or computer, individually or collectively, to operate as a special-purpose machine or computer to perform the operations carried out by the hardware components and procedures described above. For example, the instructions or software may include machine code that is executed directly by the processor or computer, such as machine code generated by a compiler.In one example, the instructions or software contain at least one of the following: an applet, a dynamic link library (DLL), middleware, firmware, a device driver, or an application program that stores the noise reduction procedure for an urban air mobility (UAM) system. In another example, the instructions or software contain higher-level code that is executed by the processor or computer using an interpreter. Average programmers can readily write the instructions or software based on the block diagrams and flowcharts shown in the drawings and the corresponding descriptions in the specification, which reveal algorithms for performing the operations carried out by the hardware components and the procedures described above. The instructions or software for controlling a processor or computer to implement the hardware components and to perform the procedures described above, as well as all related data, data files and data structures, are recorded, stored or fixed in or on one or more non-transitory, computer-readable storage media. Examples of non-transient, computer-readable storage media include read-only memory (ROM), programmable random-access memory (PROM), electrically erasable programmable random-access memory (EEPROM), random-access memory (RAM), magnetic RAM (MRAM), spin-transfer-torque (STT) MRAM, static random-access memory (SRAM), thyristor RAM (TRAM), zero-capacitor RAM (ZRAM), twin-transistor RAM (TTRAM), conductive-bridging RAM (CBRAM), ferroelectric RAM (FeRAM), phase-change RAM (PRAM), resistive RAM (RRAM), nanotube RRAM, and polymer RAM (PoRAM).Nano-floating-gate memory (NFGM), holographic memory, molecular electronic storage device, isolator-based variable resistance memory, dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), flash memory, a card-like storage device such as a multimedia microcard or card (e.g., Secure Digital (SD) or Extreme Digital (XD)), magnetic tapes, floppy disks, magneto-optical data storage devices, magnetic tapes, floppy disks, magneto-optical data storage devices, Hard disks, solid-state disks, and any other device designed to store instructions or software and all associated data,Stores data files and data structures in a non-transitory manner and makes the instructions or software and all associated data, data files, and data structures available to a processor or computer so that the processor or computer can execute the instructions. In one example, the instructions or software and all associated data, data files, and data structures are distributed across networked computer systems, so that the instructions and software and all associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers. Although this disclosure contains specific examples, it is evident from an understanding of the disclosure of this application that various changes in form and detail can be made to these examples without altering the nature and scope of the claims and their equivalents. The examples described here are descriptive only and are not to be understood as limiting. Descriptions of features and aspects in each example are to be considered applicable to similar features or aspects in other examples. Suitable results can be obtained if the described techniques are carried out in a different sequence and / or if components in a described system, architecture, device, or circuit are combined in a different way and / or replaced or supplemented by other components or their equivalents. Therefore, the scope of the disclosure is not defined by the detailed description, but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents are to be interpreted as being contained in the disclosure.

Claims

Noise reduction system for an urban air mobility system (UAM, 100) comprising: the UAM (100) configured to detect and provide information on the revolutions per minute (RPM) of a propeller and location coordinate information; a noise suppression device (200) configured to predict a noise suppression sound wave amplitude based on the RPM information of the propeller and the location coordinate information received via the UAM (100), and to output a noise suppression sound wave corresponding to the predicted noise suppression sound wave amplitude to the UAM (100); and a microphone (400) configured to receive noise generated in response to the takeoff or landing of the UAM (100), characterized by a noise suppression sound wave calculator (500) configured to calculate an offset wave using the noise received by the microphone (400);and a verifier (600) trained to verify a predicted noise suppression sound wave amplitude with the offset wave and to manage the verified noise suppression sound wave amplitude by storing the noise suppression sound wave amplitude in a database. UAM noise reduction system according to claim 1, wherein the noise reduction device (200) comprises: a communicator (210) configured to receive the RPM information of the propeller and the location coordinate information by communicating with the UAM (100); a deep learning machine (220) configured to predict the noise reduction sound wave amplitude based on the RPM information of the propeller and the location coordinate information of the UAM (100) received via the communicator (200); one or more loudspeakers (230) configured to output the noise reduction sound wave; and an output controller (240) configured to control the one or more loudspeakers (230) such that they output the noise reduction sound wave amplitude predicted by the deep learning machine (220). UAM noise reduction system according to claim 2, further comprising a weather information input device (300) configured to receive weather information, wherein the deep learning machine (220) predicts the noise reduction sound wave amplitude based on the RPM information of the propeller, the location coordinate information of the UAM (100) and the weather information. Processor-implemented noise reduction procedure for an urban air mobility system (UAM, 100), comprising: receiving information on the revolutions per minute (RPM) of a propeller and location information from a UAM (100); determining whether the collected data are sufficient by analyzing an interaction between the RPM information and the location information, which are factors received from the UAM (100); predicting a noise reduction sound wave amplitude by applying the received factors to a deep learning machine (220) in response to the finding that the collected data are sufficient; storing information on the factors used in predicting the noise reduction sound wave amplitude in a database in response to the finding that the collected data meet a reference reliability; and outputting the predicted noise reduction sound wave amplitude via a loudspeaker (230).and receiving noise information by means of a microphone (400), characterized by calculating a noise suppression sound wave amplitude using the noise information acquired via the microphone (400), in response to the finding that the collected data are insufficient, wherein, upon finding that the collected data meet a reference reliability, the predicted noise suppression sound wave amplitude stored in the database is verified by comparison with an offset wave, the offset wave being calculated from the noise information received by means of the microphone. UAM noise reduction method according to claim 4, further comprising: determining whether weather information is being input; and analyzing the interaction between the factors using the propeller's RPM information and the location information received by the UAM (100) and the weather information in response to the determination that the weather information is being input. UAM noise reduction method according to claim 5, further comprising receiving weather information from a weather authority server by web crawling or deep learning of weather information using the database, in response to the finding that no weather information is being entered.

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